Parallel transmission pulse design with explicit control for the specific absorption rate in the presence of radiofrequency errors

PURPOSE

A new framework for the design of parallel transmit (pTx) pulses is presented introducing constraints for local and global specific absorption rate (SAR) in the presence of errors in the radiofrequency (RF) transmit chain.

METHODS

The first step is the design of a pTx RF pulse with explicit constraints for global and local SAR. Then, the worst possible SAR associated with that pulse due to RF transmission errors ("worst-case SAR") is calculated. Finally, this information is used to re-calculate the pulse with lower SAR constraints, iterating this procedure until its worst-case SAR is within safety limits.

RESULTS

Analysis of an actual pTx RF transmit chain revealed amplitude errors as high as 8% (20%) and phase errors above 3° (15°) for spokes (spiral) pulses. Simulations show that using the proposed framework, pulses can be designed with controlled "worst-case SAR" in the presence of errors of this magnitude at minor cost of the excitation profile quality.

CONCLUSION

Our worst-case SAR-constrained pTx design strategy yields pulses with local and global SAR within the safety limits even in the presence of RF transmission errors. This strategy is a natural way to incorporate SAR safety factors in the design of pTx pulses. Magn Reson Med 75:2493-2504, 2016. © 2015 Wiley Periodicals, Inc.

Multi-atlas and label fusion approach for patient-specific MRI based skull estimation

PURPOSE

MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume.

METHODS

The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms.

RESULTS

The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers.

CONCLUSION

It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR.

General design approach and practical realization of decoupling matrices for parallel transmission coils

PURPOSE

In a coupled parallel transmit (pTx) array, the power delivered to a channel is partially distributed to other channels because of coupling. This power is dissipated in circulators resulting in a significant reduction in power efficiency. In this study, a technique for designing robust decoupling matrices interfaced between the RF amplifiers and the coils is proposed. The decoupling matrices ensure that most forward power is delivered to the load without loss of encoding capabilities of the pTx array.

THEORY AND METHODS

The decoupling condition requires that the impedance matrix seen by the power amplifiers is a diagonal matrix whose entries match the characteristic impedance of the power amplifiers. In this work, the impedance matrix of the coupled coils is diagonalized by a successive multiplication by its eigenvectors. A general design procedure and software are developed to generate automatically the hardware that implements diagonalization using passive components.

RESULTS

The general design method is demonstrated by decoupling two example parallel transmit arrays. Our decoupling matrices achieve better than -20 db decoupling in both cases.

CONCLUSION

A robust framework for designing decoupling matrices for pTx arrays is presented and validated. The proposed decoupling strategy theoretically scales to any arbitrary number of channels. Magn Reson Med 76:329-339, 2016. © 2015 Wiley Periodicals, Inc.

Parallel transmit pulse design for patients with deep brain stimulation implants

PURPOSE

Specific absorption rate (SAR) amplification around active implantable medical devices during diagnostic MRI procedures poses a potential risk for patient safety. In this study, we present a parallel transmit (pTx) strategy that can be used to safely scan patients with deep brain stimulation (DBS) implants.

METHODS

We performed electromagnetic simulations at 3T using a uniform phantom and a multitissue realistic head model with a generic DBS implant. Our strategy is based on using implant-friendly modes, which are defined as the modes of an array that reduce the local SAR around the DBS lead tip. These modes are used in a spokes pulse design algorithm in order to produce highly uniform magnitude least-squares flip angle excitations.

RESULTS

Local SAR (1 g) at the lead tip is reduced below 0.1 W/kg compared with 31.2 W/kg, which is obtained by a simple quadrature birdcage excitation without any sort of SAR mitigation. For the multitissue realistic head model, peak 10 g local SAR and global SAR are obtained as 4.52 W/kg and 0.48 W/kg, respectively. A uniform axial flip angle is also obtained (NRMSE <3%).

CONCLUSION

Parallel transmit arrays can be used to generate implant-friendly modes and to reduce SAR around DBS implants while constraining peak local SAR and global SAR and maximizing flip angle homogeneity.

SAR reduction in 7T C-spine imaging using a "dark modes" transmit array strategy

PURPOSE

Local specific absorption rate (SAR) limits many applications of parallel transmit (pTx) in ultra high-field imaging. In this Note, we introduce the use of an array element, which is intentionally inefficient at generating spin excitation (a "dark mode") to attempt a partial cancellation of the electric field from those elements that do generate excitation. We show that adding dipole elements oriented orthogonal to their conventional orientation to a linear array of conventional loop elements can lower the local SAR hotspot in a C-spine array at 7 T.

METHODS

We model electromagnetic fields in a head/torso model to calculate SAR and excitation B1 (+) patterns generated by conventional loop arrays and loop arrays with added electric dipole elements. We utilize the dark modes that are generated by the intentional and inefficient orientation of dipole elements in order to reduce peak 10g local SAR while maintaining excitation fidelity.

RESULTS

For B1 (+) shimming in the spine, the addition of dipole elements did not significantly alter the B1 (+) spatial pattern but reduced local SAR by 36%.

CONCLUSION

The dipole elements provide a sufficiently complimentary B1 (+) and electric field pattern to the loop array that can be exploited by the radiofrequency shimming algorithm to reduce local SAR.

Quantitative oxygen extraction fraction from 7-Tesla MRI phase: reproducibility and application in multiple sclerosis

Quantitative oxygen extraction fraction (OEF) in cortical veins was studied in patients with multiple sclerosis (MS) and healthy subjects via magnetic resonance imaging (MRI) phase images at 7 Tesla (7 T). Flow-compensated, three-dimensional gradient-echo scans were acquired for absolute OEF quantification in 23 patients with MS and 14 age-matched controls. In patients, we collected T2*-weighted images for characterization of white matter, deep gray matter, and cortical lesions, and also assessed cognitive function. Variability of OEF across readers and scan sessions was evaluated in a subset of volunteers. OEF was averaged from 2 to 3 pial veins in the sensorimotor, parietal, and prefrontal cortical regions for each subject (total of ~10 vessels). We observed good reproducibility of mean OEF, with intraobserver coefficient of variation (COV)=2.1%, interobserver COV=5.2%, and scan-rescan COV=5.9%. Patients exhibited a 3.4% reduction in cortical OEF relative to controls (P=0.0025), which was not different across brain regions. Although oxygenation did not relate with measures of structural tissue damage, mean OEF correlated with a global measure of information processing speed. These findings suggest that cortical OEF from 7-T MRI phase is a reproducible metabolic biomarker that may be sensitive to different pathologic processes than structural MRI in patients with MS.

Quantitative susceptibility mapping by inversion of a perturbation field model: correlation with brain iron in normal aging

There is increasing evidence that iron deposition occurs in specific regions of the brain in normal aging and neurodegenerative disorders such as Parkinson's, Huntington's, and Alzheimer's disease. Iron deposition changes the magnetic susceptibility of tissue, which alters the MR signal phase, and allows estimation of susceptibility differences using quantitative susceptibility mapping (QSM). We present a method for quantifying susceptibility by inversion of a perturbation model, or "QSIP." The perturbation model relates phase to susceptibility using a kernel calculated in the spatial domain, in contrast to previous Fourier-based techniques. A tissue/air susceptibility atlas is used to estimate B0 inhomogeneity. QSIP estimates in young and elderly subjects are compared to postmortem iron estimates, maps of the Field-Dependent Relaxation Rate Increase, and the L1-QSM method. Results for both groups showed excellent agreement with published postmortem data and in vivo FDRI: statistically significant Spearman correlations ranging from Rho=0.905 to Rho=1.00 were obtained. QSIP also showed improvement over FDRI and L1-QSM: reduced variance in susceptibility estimates and statistically significant group differences were detected in striatal and brainstem nuclei, consistent with age-dependent iron accumulation in these regions.

Fast quantitative susceptibility mapping with L1-regularization and automatic parameter selection

PURPOSE

To enable fast reconstruction of quantitative susceptibility maps with total variation penalty and automatic regularization parameter selection.

METHODS

ℓ(1) -Regularized susceptibility mapping is accelerated by variable splitting, which allows closed-form evaluation of each iteration of the algorithm by soft thresholding and fast Fourier transforms. This fast algorithm also renders automatic regularization parameter estimation practical. A weighting mask derived from the magnitude signal can be incorporated to allow edge-aware regularization.

RESULTS

Compared with the nonlinear conjugate gradient (CG) solver, the proposed method is 20 times faster. A complete pipeline including Laplacian phase unwrapping, background phase removal with SHARP filtering, and ℓ(1) -regularized dipole inversion at 0.6 mm isotropic resolution is completed in 1.2 min using MATLAB on a standard workstation compared with 22 min using the CG solver. This fast reconstruction allows estimation of regularization parameters with the L-curve method in 13 min, which would have taken 4 h with the CG algorithm. The proposed method also permits magnitude-weighted regularization, which prevents smoothing across edges identified on the magnitude signal. This more complicated optimization problem is solved 5 times faster than the nonlinear CG approach. Utility of the proposed method is also demonstrated in functional blood oxygen level-dependent susceptibility mapping, where processing of the massive time series dataset would otherwise be prohibitive with the CG solver.

CONCLUSION

Online reconstruction of regularized susceptibility maps may become feasible with the proposed dipole inversion.

Regional quantification of cerebral venous oxygenation from MRI susceptibility during hypercapnia

There is an unmet medical need for noninvasive imaging of regional brain oxygenation to manage stroke, tumor, and neurodegenerative diseases. Oxygenation imaging from magnetic susceptibility in MRI is a promising new technique to measure local venous oxygen extraction fraction (OEF) along the cerebral venous vasculature. However, this approach has not been tested in vivo at different levels of oxygenation. The primary goal of this study was to test whether susceptibility imaging of oxygenation can detect OEF changes induced by hypercapnia, via CO2 inhalation, within selected a priori brain regions. Ten healthy subjects were scanned at 3T with a 32-channel head coil. The end-tidal CO2 (ETCO2) was monitored continuously and inspired gases were adjusted to achieve steady-state conditions of eucapnia (41±3mmHg) and hypercapnia (50±4mmHg). Gradient echo phase images and pseudo-continuous arterial spin labeling (pcASL) images were acquired to measure regional OEF and CBF respectively during eucapnia and hypercapnia. By assuming constant cerebral oxygen consumption throughout both gas states, regional CBF values were computed to predict the local change in OEF in each brain region. Hypercapnia induced a relative decrease in OEF of -42.3% in the straight sinus, -39.9% in the internal cerebral veins, and approximately -50% in pial vessels draining each of the occipital, parietal, and frontal cortical areas. Across volunteers, regional changes in OEF correlated with changes in ETCO2. The reductions in regional OEF (via phase images) were significantly correlated (P<0.05) with predicted reductions in OEF derived from CBF data (via pcASL images). These findings suggest that susceptibility imaging is a promising technique for OEF measurements, and may serve as a clinical biomarker for brain conditions with aberrant regional oxygenation.

Fast group matching for MR fingerprinting reconstruction

PURPOSE

MR fingerprinting (MRF) is a technique for quantitative tissue mapping using pseudorandom measurements. To estimate tissue properties such as T1 , T2 , proton density, and B0 , the rapidly acquired data are compared against a large dictionary of Bloch simulations. This matching process can be a very computationally demanding portion of MRF reconstruction.

THEORY AND METHODS

We introduce a fast group matching algorithm (GRM) that exploits inherent correlation within MRF dictionaries to create highly clustered groupings of the elements. During matching, a group specific signature is first used to remove poor matching possibilities. Group principal component analysis (PCA) is used to evaluate all remaining tissue types. In vivo 3 Tesla brain data were used to validate the accuracy of our approach.

RESULTS

For a trueFISP sequence with over 196,000 dictionary elements, 1000 MRF samples, and image matrix of 128 × 128, GRM was able to map MR parameters within 2s using standard vendor computational resources. This is an order of magnitude faster than global PCA and nearly two orders of magnitude faster than direct matching, with comparable accuracy (1-2% relative error).

CONCLUSION

The proposed GRM method is a highly efficient model reduction technique for MRF matching and should enable clinically relevant reconstruction accuracy and time on standard vendor computational resources.

Accelerated 1 H MRSI using randomly undersampled spiral-based k-space trajectories

PURPOSE

To develop and evaluate the performance of an acquisition and reconstruction method for accelerated MR spectroscopic imaging (MRSI) through undersampling of spiral trajectories.

THEORY AND METHODS

A randomly undersampled spiral acquisition and sensitivity encoding (SENSE) with total variation (TV) regularization, random SENSE+TV, is developed and evaluated on single-slice numerical phantom, in vivo single-slice MRSI, and in vivo three-dimensional (3D)-MRSI at 3 Tesla. Random SENSE+TV was compared with five alternative methods for accelerated MRSI.

RESULTS

For the in vivo single-slice MRSI, random SENSE+TV yields up to 2.7 and 2 times reduction in root-mean-square error (RMSE) of reconstructed N-acetyl aspartate (NAA), creatine, and choline maps, compared with the denoised fully sampled and uniformly undersampled SENSE+TV methods with the same acquisition time, respectively. For the in vivo 3D-MRSI, random SENSE+TV yields up to 1.6 times reduction in RMSE, compared with uniform SENSE+TV. Furthermore, by using random SENSE+TV, we have demonstrated on the in vivo single-slice and 3D-MRSI that acceleration factors of 4.5 and 4 are achievable with the same quality as the fully sampled data, as measured by RMSE of reconstructed NAA map, respectively.

CONCLUSION

With the same scan time, random SENSE+TV yields lower RMSEs of metabolite maps than other methods evaluated. Random SENSE+TV achieves up to 4.5-fold acceleration with comparable data quality as the fully sampled acquisition. Magn Reson Med 74:13-24, 2015. © 2014 Wiley Periodicals, Inc.

Fast image reconstruction with L2-regularization

PURPOSE

We introduce L2-regularized reconstruction algorithms with closed-form solutions that achieve dramatic computational speed-up relative to state of the art L1- and L2-based iterative algorithms while maintaining similar image quality for various applications in MRI reconstruction.

MATERIALS AND METHODS

We compare fast L2-based methods to state of the art algorithms employing iterative L1- and L2-regularization in numerical phantom and in vivo data in three applications; (i) Fast Quantitative Susceptibility Mapping (QSM), (ii) Lipid artifact suppression in Magnetic Resonance Spectroscopic Imaging (MRSI), and (iii) Diffusion Spectrum Imaging (DSI). In all cases, proposed L2-based methods are compared with the state of the art algorithms, and two to three orders of magnitude speed up is demonstrated with similar reconstruction quality.

RESULTS

The closed-form solution developed for regularized QSM allows processing of a three-dimensional volume under 5 s, the proposed lipid suppression algorithm takes under 1 s to reconstruct single-slice MRSI data, while the PCA based DSI algorithm estimates diffusion propagators from undersampled q-space for a single slice under 30 s, all running in Matlab using a standard workstation.

CONCLUSION

For the applications considered herein, closed-form L2-regularization can be a faster alternative to its iterative counterpart or L1-based iterative algorithms, without compromising image quality.

Comparison of simulated parallel transmit body arrays at 3 T using excitation uniformity, global SAR, local SAR, and power efficiency metrics

PURPOSE

We compare the performance of eight parallel transmit (pTx) body arrays with up to 32 channels and a standard birdcage design. Excitation uniformity, local specific absorption rate (SAR), global SAR, and power metrics are analyzed in the torso at 3 T for radiofrequency (RF)-shimming and 2-spoke excitations.

METHODS

We used a fast cosimulation strategy for field calculation in the presence of coupling between transmit channels. We designed spoke pulses using magnitude least squares optimization with explicit constraint of SAR and power and compared the performance of the different pTx coils using the L-curve method.

RESULTS

PTx arrays outperformed the conventional birdcage coil in all metrics except peak and average power efficiency. The presence of coupling exacerbated this power efficiency problem. At constant excitation fidelity, the pTx array with 24 channels arranged in three z-rows could decrease local SAR more than 4-fold (2-fold) for RF-shimming (2-spoke) compared to the birdcage coil for pulses of equal duration. Multi-row pTx coils had a marked performance advantage compared to single row designs, especially for coronal imaging.

CONCLUSION

PTx coils can simultaneously improve the excitation uniformity and reduce SAR compared to a birdcage coil when SAR metrics are explicitly constrained in the pulse design.

Local specific absorption rate (SAR), global SAR, transmitter power, and excitation accuracy trade-offs in low flip-angle parallel transmit pulse design

PURPOSE

We propose a constrained optimization approach for designing parallel transmit (pTx) pulses satisfying all regulatory and hardware limits. We study the trade-offs between excitation accuracy, local and global specific absorption rate (SAR), and maximum and average power for small flip-angle pTx (eight channels) spokes pulses in the torso at 3 T and in the head at 7 T.

METHODS

We compare the trade-offs between the above-mentioned quantities using the L-curve method. We use a primal-dual algorithm and a compressed set of local SAR matrices to design radio-frequency (RF) pulses satisfying all regulatory (including local SAR) and hardware constraints.

RESULTS

Local SAR can be substantially reduced (factor of 2 or more) by explicitly constraining it in the pulse design process compared to constraining global SAR or pulse power alone. This often comes at the price of increased pulse power.

CONCLUSION

Simultaneous control of power and SAR is needed for the design of pTx pulses that are safe and can be played on the scanner. Constraining a single quantity can create large increase in the others, which can then rise above safety or hardware limits. Simultaneous constraint of local SAR and power is fast enough to be applicable in a clinical setting.

Fast reconstruction for multichannel compressed sensing using a hierarchically semiseparable solver

PURPOSE

The adoption of multichannel compressed sensing (CS) for clinical magnetic resonance imaging (MRI) hinges on the ability to accurately reconstruct images from an undersampled dataset in a reasonable time frame. When CS is combined with SENSE parallel imaging, reconstruction can be computationally intensive. As an alternative to iterative methods that repetitively evaluate a forward CS+SENSE model, we introduce a technique for the fast computation of a compact inverse model solution.

METHODS

A recently proposed hierarchically semiseparable (HSS) solver is used to compactly represent the inverse of the CS+SENSE encoding matrix to a high level of accuracy. To investigate the computational efficiency of the proposed HSS-Inverse method, we compare reconstruction time with the current state-of-the-art. In vivo 3T brain data at multiple image contrasts, resolutions, acceleration factors, and number of receive channels were used for this comparison.

RESULTS

The HSS-Inverse method allows for >6× speedup when compared to current state-of-the-art reconstruction methods with the same accuracy. Efficient computational scaling is demonstrated for CS+SENSE with respect to image size. The HSS-Inverse method is also shown to have minimal dependency on the number of parallel imaging channels/acceleration factor.

CONCLUSIONS

The proposed HSS-Inverse method is highly efficient and should enable real-time CS reconstruction on standard MRI vendors' computational hardware.

Correlation of oxygenation and perfusion sensitive MRI with invasive micro probe measurements in healthy mice brain

The non-invasive assessment of (patho-)physiological parameters such as, perfusion and oxygenation, is of great importance for the characterization of pathologies e.g., tumors, which may be helpful to better predict treatment response and potential outcome. To better understand the influence of physiological parameters on the investigated oxygenation and perfusion sensitive MRI methods, MRI measurements were correlated with subsequent invasive micro probe measurements during free breathing conditions of air, air+10% CO2 and 100% O2 in healthy mice brain. MRI parameters were the irreversible (R2), reversible (R2') and effective (R2*) transverse relaxation rates, venous blood oxygenation level assessed by quantitative blood oxygenation level dependent (qBOLD) method and cerebral blood flow (CBF) assessed by arterial spin labeling (ASL) using a 7 T small animal MRI scanner. One to two days after MRI, tissue perfusion and pO2 were measured by Laser-Doppler flowmetry and fluorescence quenching micro probes, respectively. The tissue pO2 values were converted to blood oxygen saturation by using the Hill equation. The animals were anesthetized by intra peritoneal injection of ketamine-xylazine-acepromazine (10-2-0.3 mg/ml · kg). Results for normal/hypercapnia/hyperoxia conditions were: R2[s(∧)-1] = 20.7/20.4/20.1, R2*[s(∧)-1] = 31.6/29.6/25.9, R2'[s-(∧)1] = 10.9/9.2/5.7, qBOLD venous blood oxygenation level = 0.43/0.51/0.56, CBF[ml · min(∧)-1 · 100 g(∧)-1] = 70.6/105.5/81.8, Laser-Doppler flowmetry[a.u.] = 89.2/120.2/90.6 and pO2[mmHg] = 6.3/32.3/46.7. All parameters were statistically significantly different with P < 0.001 between all breathing conditions. All MRI and the corresponding micro probe measurements were also statistically significantly (P ≤ 0.03) correlated with each other. However, converting the tissue pO2 to blood oxygen saturation = 0.02/0.34/0.63, showed only very limited agreement with the qBOLD venous blood oxygenation level. We found good correlation between MRI and micro probe measurements. However, direct conversion of tissue pO2 to blood oxygen saturation by using the Hill equation is very limited. Furthermore, adverse effects of anesthesia and trauma due to micro probe insertion are strong confounding factors and need close attention for study planning and conduction of experiments. Investigation of the correlation of perfusion and oxygenation sensitive MRI methods with micro probe measurements in pathologic tissue such as tumors is now of compelling interest.

Fast dictionary-based reconstruction for diffusion spectrum imaging

Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using MATLAB running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using principal component analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm.

Quantitative oxygenation venography from MRI phase

PURPOSE

To demonstrate acquisition and processing methods for quantitative oxygenation venograms that map in vivo oxygen saturation (SvO2 ) along cerebral venous vasculature.

METHODS

Regularized quantitative susceptibility mapping (QSM) is used to reconstruct susceptibility values and estimate SvO2 in veins. QSM with ℓ1 and ℓ2 regularization are compared in numerical simulations of vessel structures with known magnetic susceptibility. Dual-echo, flow-compensated phase images are collected in three healthy volunteers to create QSM images. Bright veins in the susceptibility maps are vectorized and used to form a three-dimensional vascular mesh, or venogram, along which to display SvO2 values from QSM.

RESULTS

Quantitative oxygenation venograms that map SvO2 along brain vessels of arbitrary orientation and geometry are shown in vivo. SvO2 values in major cerebral veins lie within the normal physiological range reported by (15) O positron emission tomography. SvO2 from QSM is consistent with previous MR susceptometry methods for vessel segments oriented parallel to the main magnetic field. In vessel simulations, ℓ1 regularization results in less than 10% SvO2 absolute error across all vessel tilt orientations and provides more accurate SvO2 estimation than ℓ2 regularization.

CONCLUSION

The proposed analysis of susceptibility images enables reliable mapping of quantitative SvO2 along venograms and may facilitate clinical use of venous oxygenation imaging.

Sparsity-promoting calibration for GRAPPA accelerated parallel MRI reconstruction

The amount of calibration data needed to produce images of adequate quality can prevent auto-calibrating parallel imaging reconstruction methods like generalized autocalibrating partially parallel acquisitions (GRAPPA) from achieving a high total acceleration factor. To improve the quality of calibration when the number of auto-calibration signal (ACS) lines is restricted, we propose a sparsity-promoting regularized calibration method that finds a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images. Several experiments evaluate the performance of the proposed method relative to unregularized and existing regularized calibration methods for both low-quality and underdetermined fits from the ACS lines. These experiments demonstrate that the proposed method, like other regularization methods, is capable of mitigating noise amplification, and in addition, the proposed method is particularly effective at minimizing coherent aliasing artifacts caused by poor kernel calibration in real data. Using the proposed method, we can increase the total achievable acceleration while reducing degradation of the reconstructed image better than existing regularized calibration methods.

Accelerated diffusion spectrum imaging with compressed sensing using adaptive dictionaries

Diffusion spectrum imaging offers detailed information on complex distributions of intravoxel fiber orientations at the expense of extremely long imaging times (∼1 h). Recent work by Menzel et al. demonstrated successful recovery of diffusion probability density functions from sub-Nyquist sampled q-space by imposing sparsity constraints on the probability density functions under wavelet and total variation transforms. As the performance of compressed sensing reconstruction depends strongly on the level of sparsity in the selected transform space, a dictionary specifically tailored for diffusion probability density functions can yield higher fidelity results. To our knowledge, this work is the first application of adaptive dictionaries in diffusion spectrum imaging, whereby we reduce the scan time of whole brain diffusion spectrum imaging acquisition from 50 to 17 min while retaining high image quality. In vivo experiments were conducted with the 3T Connectome MRI. The root-mean-square error of the reconstructed "missing" diffusion images were calculated by comparing them to a gold standard dataset (obtained from acquiring 10 averages of diffusion images in these missing directions). The root-mean-square error from the proposed reconstruction method is up to two times lower than that of Menzel et al.'s method and is actually comparable to that of the fully-sampled 50 minute scan. Comparison of tractography solutions in 18 major white-matter pathways also indicated good agreement between the fully-sampled and 3-fold accelerated reconstructions. Further, we demonstrate that a dictionary trained using probability density functions from a single slice of a particular subject generalizes well to other slices from the same subject, as well as to slices from other subjects.